| 000 | 03645naaaa2200877uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/76345 | ||
| 005 | 20220714184559.0 | ||
| 020 | _abooks978-3-0365-1206-8 | ||
| 020 | _a9783036512075 | ||
| 020 | _a9783036512068 | ||
| 024 | 7 |
_a10.3390/books978-3-0365-1206-8 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aTB _2bicssc |
|
| 100 | 1 |
_aDeschrijver, Dirk _4edt _91605869 |
|
| 700 | 1 |
_aDeschrijver, Dirk _4oth _91605869 |
|
| 245 | 1 | 0 | _aImproving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization |
| 260 |
_aBasel, Switzerland _bMDPI - Multidisciplinary Digital Publishing Institute _c2021 |
||
| 300 | _a1 electronic resource (201 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aIn October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/ |
||
| 546 | _aEnglish | ||
| 650 | 7 |
_aTechnology: general issues _2bicssc _9928609 |
|
| 653 | _apassive house | ||
| 653 | _aenclosure structure | ||
| 653 | _aheat transfer coefficient | ||
| 653 | _aenergy consumption | ||
| 653 | _aturbo-propeller | ||
| 653 | _aregional | ||
| 653 | _afuel | ||
| 653 | _aweight | ||
| 653 | _arange | ||
| 653 | _adesign | ||
| 653 | _aCO2 reduction | ||
| 653 | _amulti-objective combinatorial optimization | ||
| 653 | _ameta-heuristics | ||
| 653 | _aant colony optimization | ||
| 653 | _anon-intrusive load monitoring | ||
| 653 | _aappliance classification | ||
| 653 | _aappliance feature | ||
| 653 | _arecurrence graph | ||
| 653 | _aweighted recurrence graph | ||
| 653 | _aV-I trajectory | ||
| 653 | _aconvolutional neural network | ||
| 653 | _aenergy baselines | ||
| 653 | _amachine learning | ||
| 653 | _aclustering | ||
| 653 | _aneural methods | ||
| 653 | _asmart intelligent systems | ||
| 653 | _abuilding energy consumption | ||
| 653 | _abuilding load forecasting | ||
| 653 | _aenergy efficiency | ||
| 653 | _athermal improved of buildings | ||
| 653 | _aanti-icing | ||
| 653 | _aheat and mass transfer | ||
| 653 | _aheating power distribution | ||
| 653 | _aheat load reduction | ||
| 653 | _aoptimization method | ||
| 653 | _aexperimental validation | ||
| 653 | _abig data process | ||
| 653 | _apredictive maintenance | ||
| 653 | _afracturing roofs to maintain entry (FRME) | ||
| 653 | _afield measurement | ||
| 653 | _anumerical simulation | ||
| 653 | _aside abutment pressure | ||
| 653 | _astrata movement | ||
| 653 | _aenergy | ||
| 653 | _amanufacturing | ||
| 653 | _aprediction | ||
| 653 | _aforecasting | ||
| 653 | _amodelling | ||
| 653 | _an/a | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/3770 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/76345 _70 _zDOAB: description of the publication |
| 999 |
_c3006432 _d3006432 |
||